Weather generators have been very used in agriculture climate-change impact assessments (Hoogenboom, 2000; Sivakumar, 2001). A weather generator produces synthetic daily time series of climatic variables statistically equivalent to the recorded historical series, as well as daily site-specific climate scenarios that could be based on regional GCM results (Semenov and Jamieson, 2001). The weather generator usually mimics correctly the mean values of the climatic variables, although underestimates their variability (Mavromatis and Jones, 1998; Semenov and Jamieson, 2001; Wilby and Wigley, 2001). Different weather generators are available, but according to Wilby and Wigley (2001), the US-made and the UK-made WGEN and LARS-WG are the most widely used.
LARS-WG is a stochastic weather generator which can be used for the simulation of weather data at a single site (Racsko et al, 1991; Semenov et al, 1998; Semenov and Brooks, 1999; Semenov and Barrow, 2002), under both current and future climate conditions. These data are in the form of daily time-series for a suite of climate variables, namely, precipitation, maximum and minimum temperature and solar radiation.
According to Semenov and Barrow (2002), stochastic weather generators were originally developed for two main purposes:
1. To provide a means of simulating synthetic weather time-series with statistical characteristics corresponding to the observed statistics at a site, but which were long enough to be used in an assessment of risk in hydrological or agricultural applications.
2. To provide a means of extending the simulation of weather time-series to unobserved locations, through the interpolation of the weather generator parameters obtained from running the models at neighbouring sites.
A stochastic weather generator is not a predictive tool, but simply a mean to generate time-series of synthetic weather statistically 'identical' to the observations. A stochastic weather generator, however, can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which incorporate changes in both mean climate and in climate variability (Semenov and Barrow, 1997).
The LARS-WG weather generator focused to overcome the limitations of the Markov chain model of precipitation occurrence (Richardson and Wright, 1984). This widely used method of modelling precipitation occurrence (which generally considers two precipitation states, wet or dry, and considers conditions on the previous day only) is not always able to correctly simulate the maximum dry spell length. LARS-WG follows a 'series' approach, in which the simulation of dry and wet spell length is the first step in the weather generation process.
The most recent version of LARS-WG (version 3.0 for Windows 9x/NT/2000/XP) has undergone a complete redevelopment in order to produce a robust model capable of generating synthetic weather data for a wide range of climates (Semenov and Barrow, 2002). LARS-WG has been compared with another widely-used stochastic weather generator, which uses the Markov chain approach (WGEN; Richardson, 1985; Richardson and Wright, 1984), at a number of sites representing diverse climates and has been shown to perform at least as well as, if not better than, WGEN at each of these sites (Semenov et al, 1998).
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